Large datasets are important for implementing machine learning (ML) solutions. In particular, many methods use models having large numbers of parameters, which often require large amounts of data for optimization. Supervised learning relies upon these large datasets being explicitly labeled. However, labeling the data is typically performed with a human in the loop, whether partially (e.g., checked) by human or performed entirely manually by a human. This aspect can make the generation of suitable datasets time consuming and expensive. Furthermore, labeled datasets that are collected from real world scenarios are subject to the random nature of real world events. Accordingly, collecting real-world datasets with suitable coverage of the parameter space underlying a model can be prohibitively time consuming and complex, and in many cases the coverage is difficult to quantify or determine precisely. In addition, methods for producing synthetic datasets to mitigate issues with real datasets often employ a virtual world (e.g., a single virtual world, a limited number of holistically-generated virtual worlds, etc.) in which a virtual camera positioned and used to generate synthetic images. However, this approach can yield datasets where some environmental parameters are constant or inadequately varied throughout, due to the limited nature of the virtual world. The resultant datasets can also suffer from: poor image quality, poor realism (e.g., manifesting as a large “domain shift” between the real and synthetic images), requiring “fine tuning” (e.g., training on synthetic, then real, images to fine tune the neural networks, adjusting the training data manually to improve performance, etc.), one-off virtual world creation (e.g., generalized scene configurations corresponding to an explorable virtual world instead of unique scene configurations), and insufficient parameter variation (e.g., varying too few image parameters, such as time of day, weather, etc.) Often, the architecture of computational paradigms for generating typical virtual worlds (e.g., gaming engines) do not adequately generate wide and efficient variability over the underlying parameter space, due to the divergence between the needs of common use cases (e.g., game play) and those of synthetic datasets for ML model use.
Thus, there is a need in the field to create a new and useful method of generating synthetic image datasets depicting simulated real-world imagery, that are intrinsically labeled, and efficiently cover the parameter space underlying machine learning models. This invention provides such a new and useful method.